Overview

Dataset statistics

Number of variables14
Number of observations2460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory352.8 KiB
Average record size in memory146.9 B

Variable types

Numeric9
Categorical5

Alerts

away_team_hits is highly overall correlated with away_team_runsHigh correlation
away_team_runs is highly overall correlated with away_team_hitsHigh correlation
home_team_hits is highly overall correlated with home_team_runsHigh correlation
home_team_runs is highly overall correlated with home_team_hitsHigh correlation
start_time is highly overall correlated with time_conditionHigh correlation
home_team is highly overall correlated with venue and 1 other fieldsHigh correlation
venue is highly overall correlated with home_team and 1 other fieldsHigh correlation
time_condition is highly overall correlated with start_timeHigh correlation
field_conditions is highly overall correlated with home_team and 1 other fieldsHigh correlation
field_conditions is highly imbalanced (64.2%)Imbalance
away_team is uniformly distributedUniform
home_team is uniformly distributedUniform
away_team_errors has 1407 (57.2%) zerosZeros
away_team_runs has 156 (6.3%) zerosZeros
home_team_errors has 1416 (57.6%) zerosZeros
home_team_runs has 130 (5.3%) zerosZeros

Reproduction

Analysis started2023-02-03 23:00:09.145340
Analysis finished2023-02-03 23:01:56.965153
Duration1 minute and 47.82 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

attendance
Real number (ℝ)

Distinct2374
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30370.704
Minimum8766
Maximum54449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:01:57.395749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8766
5-th percentile13971.15
Q122432
median30604.5
Q338396.25
95-th percentile45624.1
Maximum54449
Range45683
Interquartile range (IQR)15964.25

Descriptive statistics

Standard deviation9875.4667
Coefficient of variation (CV)0.32516424
Kurtosis-0.90285907
Mean30370.704
Median Absolute Deviation (MAD)8006
Skewness-0.052483633
Sum74711931
Variance97524843
MonotonicityNot monotonic
2023-02-03T17:01:58.318273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27631 3
 
0.1%
41210 2
 
0.1%
41850 2
 
0.1%
13481 2
 
0.1%
44317 2
 
0.1%
34294 2
 
0.1%
39691 2
 
0.1%
36544 2
 
0.1%
22230 2
 
0.1%
26087 2
 
0.1%
Other values (2364) 2439
99.1%
ValueCountFrequency (%)
8766 1
< 0.1%
9393 1
< 0.1%
9890 1
< 0.1%
10068 1
< 0.1%
10072 1
< 0.1%
10114 1
< 0.1%
10115 1
< 0.1%
10117 1
< 0.1%
10251 1
< 0.1%
10283 1
< 0.1%
ValueCountFrequency (%)
54449 2
0.1%
54269 1
< 0.1%
53901 1
< 0.1%
53621 1
< 0.1%
53449 1
< 0.1%
53409 1
< 0.1%
53299 1
< 0.1%
53297 1
< 0.1%
53279 1
< 0.1%
52728 1
< 0.1%

away_team
Categorical

Distinct30
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Chicago Cubs
 
90
Los Angeles Dodgers
 
87
Cleveland Indians
 
86
Toronto Blue Jays
 
85
San Francisco Giants
 
84
Other values (25)
2028 

Length

Max length29
Median length19
Mean length16.694309
Min length12

Characters and Unicode

Total characters41068
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York Mets
2nd rowPhiladelphia Phillies
3rd rowMinnesota Twins
4th rowWashington Nationals
5th rowColorado Rockies

Common Values

ValueCountFrequency (%)
Chicago Cubs 90
 
3.7%
Los Angeles Dodgers 87
 
3.5%
Cleveland Indians 86
 
3.5%
Toronto Blue Jays 85
 
3.5%
San Francisco Giants 84
 
3.4%
Boston Red Sox 83
 
3.4%
Washington Nationals 83
 
3.4%
Baltimore Orioles 82
 
3.3%
Texas Rangers 82
 
3.3%
Cincinnati Reds 81
 
3.3%
Other values (20) 1617
65.7%

Length

2023-02-03T17:01:59.191418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago 171
 
2.8%
angeles 168
 
2.8%
los 168
 
2.8%
san 165
 
2.7%
sox 164
 
2.7%
new 161
 
2.7%
york 161
 
2.7%
cubs 90
 
1.5%
dodgers 87
 
1.4%
cleveland 86
 
1.4%
Other values (57) 4646
76.6%

Most occurring characters

ValueCountFrequency (%)
a 3762
 
9.2%
3607
 
8.8%
s 3530
 
8.6%
e 3275
 
8.0%
i 3021
 
7.4%
o 2800
 
6.8%
n 2714
 
6.6%
t 2035
 
5.0%
r 1876
 
4.6%
l 1804
 
4.4%
Other values (36) 12644
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31395
76.4%
Uppercase Letter 5986
 
14.6%
Space Separator 3607
 
8.8%
Other Punctuation 80
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3762
12.0%
s 3530
11.2%
e 3275
10.4%
i 3021
9.6%
o 2800
8.9%
n 2714
8.6%
t 2035
 
6.5%
r 1876
 
6.0%
l 1804
 
5.7%
g 915
 
2.9%
Other values (14) 5663
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 670
11.2%
A 653
10.9%
B 492
 
8.2%
S 490
 
8.2%
R 489
 
8.2%
M 485
 
8.1%
T 410
 
6.8%
P 405
 
6.8%
D 330
 
5.5%
L 248
 
4.1%
Other values (10) 1314
22.0%
Space Separator
ValueCountFrequency (%)
3607
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37381
91.0%
Common 3687
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3762
 
10.1%
s 3530
 
9.4%
e 3275
 
8.8%
i 3021
 
8.1%
o 2800
 
7.5%
n 2714
 
7.3%
t 2035
 
5.4%
r 1876
 
5.0%
l 1804
 
4.8%
g 915
 
2.4%
Other values (34) 11649
31.2%
Common
ValueCountFrequency (%)
3607
97.8%
. 80
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3762
 
9.2%
3607
 
8.8%
s 3530
 
8.6%
e 3275
 
8.0%
i 3021
 
7.4%
o 2800
 
6.8%
n 2714
 
6.6%
t 2035
 
5.0%
r 1876
 
4.6%
l 1804
 
4.4%
Other values (36) 12644
30.8%

away_team_errors
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5800813
Minimum0
Maximum5
Zeros1407
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:01:59.862880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79322671
Coefficient of variation (CV)1.3674406
Kurtosis2.1811828
Mean0.5800813
Median Absolute Deviation (MAD)0
Skewness1.4582673
Sum1427
Variance0.62920861
MonotonicityNot monotonic
2023-02-03T17:02:00.623533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1407
57.2%
1 765
31.1%
2 215
 
8.7%
3 61
 
2.5%
4 11
 
0.4%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 1407
57.2%
1 765
31.1%
2 215
 
8.7%
3 61
 
2.5%
4 11
 
0.4%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 11
 
0.4%
3 61
 
2.5%
2 215
 
8.7%
1 765
31.1%
0 1407
57.2%

away_team_hits
Real number (ℝ)

Distinct22
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7670732
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:02:01.405152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q311
95-th percentile15
Maximum22
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5126873
Coefficient of variation (CV)0.40066819
Kurtosis0.13926584
Mean8.7670732
Median Absolute Deviation (MAD)2
Skewness0.51233243
Sum21567
Variance12.338972
MonotonicityNot monotonic
2023-02-03T17:02:02.146452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
9 293
11.9%
7 287
11.7%
8 275
11.2%
10 238
9.7%
6 225
9.1%
5 197
8.0%
11 194
7.9%
4 135
 
5.5%
12 132
 
5.4%
13 113
 
4.6%
Other values (12) 371
15.1%
ValueCountFrequency (%)
1 7
 
0.3%
2 26
 
1.1%
3 83
 
3.4%
4 135
5.5%
5 197
8.0%
6 225
9.1%
7 287
11.7%
8 275
11.2%
9 293
11.9%
10 238
9.7%
ValueCountFrequency (%)
22 4
 
0.2%
21 1
 
< 0.1%
20 2
 
0.1%
19 12
 
0.5%
18 15
 
0.6%
17 28
 
1.1%
16 40
 
1.6%
15 66
2.7%
14 87
3.5%
13 113
4.6%

away_team_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4150407
Minimum0
Maximum21
Zeros156
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:02:02.916776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile10
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1053905
Coefficient of variation (CV)0.70336622
Kurtosis1.0089307
Mean4.4150407
Median Absolute Deviation (MAD)2
Skewness0.93889878
Sum10861
Variance9.64345
MonotonicityNot monotonic
2023-02-03T17:02:03.604490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 347
14.1%
2 340
13.8%
4 308
12.5%
5 272
11.1%
1 265
10.8%
6 217
8.8%
7 179
7.3%
0 156
6.3%
8 115
 
4.7%
9 85
 
3.5%
Other values (10) 176
7.2%
ValueCountFrequency (%)
0 156
6.3%
1 265
10.8%
2 340
13.8%
3 347
14.1%
4 308
12.5%
5 272
11.1%
6 217
8.8%
7 179
7.3%
8 115
 
4.7%
9 85
 
3.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 5
 
0.2%
15 10
 
0.4%
14 7
 
0.3%
13 21
 
0.9%
12 25
 
1.0%
11 36
1.5%
10 69
2.8%

game_duration
Real number (ℝ)

Distinct168
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0853523
Minimum1.25
Maximum6.2166667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:02:04.432833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.25
5-th percentile2.4833333
Q12.7833333
median3.0333333
Q33.3166667
95-th percentile3.8666667
Maximum6.2166667
Range4.9666667
Interquartile range (IQR)0.53333333

Descriptive statistics

Standard deviation0.46067116
Coefficient of variation (CV)0.1493091
Kurtosis5.2599318
Mean3.0853523
Median Absolute Deviation (MAD)0.26666667
Skewness1.4760277
Sum7589.9667
Variance0.21221792
MonotonicityNot monotonic
2023-02-03T17:02:05.384832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.916666667 55
 
2.2%
2.933333333 54
 
2.2%
3 53
 
2.2%
2.9 51
 
2.1%
3.133333333 47
 
1.9%
3.066666667 47
 
1.9%
3.05 46
 
1.9%
3.083333333 46
 
1.9%
2.866666667 45
 
1.8%
3.116666667 45
 
1.8%
Other values (158) 1971
80.1%
ValueCountFrequency (%)
1.25 1
 
< 0.1%
1.916666667 1
 
< 0.1%
2.033333333 1
 
< 0.1%
2.1 1
 
< 0.1%
2.116666667 1
 
< 0.1%
2.133333333 2
0.1%
2.166666667 4
0.2%
2.183333333 3
0.1%
2.2 3
0.1%
2.216666667 2
0.1%
ValueCountFrequency (%)
6.216666667 1
< 0.1%
5.933333333 1
< 0.1%
5.8 1
< 0.1%
5.783333333 1
< 0.1%
5.566666667 1
< 0.1%
5.433333333 1
< 0.1%
5.416666667 2
0.1%
5.383333333 1
< 0.1%
5.3 1
< 0.1%
5.166666667 1
< 0.1%

home_team
Categorical

HIGH CORRELATION  UNIFORM 

Distinct30
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Cleveland Indians
 
89
Chicago Cubs
 
89
Los Angeles Dodgers
 
86
Toronto Blue Jays
 
86
Washington Nationals
 
84
Other values (25)
2026 

Length

Max length29
Median length19
Mean length16.695528
Min length12

Characters and Unicode

Total characters41071
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKansas City Royals
2nd rowCincinnati Reds
3rd rowBaltimore Orioles
4th rowAtlanta Braves
5th rowArizona Diamondbacks

Common Values

ValueCountFrequency (%)
Cleveland Indians 89
 
3.6%
Chicago Cubs 89
 
3.6%
Los Angeles Dodgers 86
 
3.5%
Toronto Blue Jays 86
 
3.5%
Washington Nationals 84
 
3.4%
Texas Rangers 83
 
3.4%
San Francisco Giants 83
 
3.4%
Boston Red Sox 82
 
3.3%
Kansas City Royals 81
 
3.3%
Cincinnati Reds 81
 
3.3%
Other values (20) 1616
65.7%

Length

2023-02-03T17:02:06.330380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago 169
 
2.8%
los 167
 
2.8%
angeles 167
 
2.8%
san 164
 
2.7%
sox 162
 
2.7%
new 162
 
2.7%
york 162
 
2.7%
indians 89
 
1.5%
cleveland 89
 
1.5%
cubs 89
 
1.5%
Other values (57) 4646
76.6%

Most occurring characters

ValueCountFrequency (%)
a 3770
 
9.2%
3606
 
8.8%
s 3530
 
8.6%
e 3277
 
8.0%
i 3014
 
7.3%
o 2795
 
6.8%
n 2724
 
6.6%
t 2033
 
4.9%
r 1872
 
4.6%
l 1810
 
4.4%
Other values (36) 12640
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31399
76.5%
Uppercase Letter 5985
 
14.6%
Space Separator 3606
 
8.8%
Other Punctuation 81
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3770
12.0%
s 3530
11.2%
e 3277
10.4%
i 3014
9.6%
o 2795
8.9%
n 2724
8.7%
t 2033
 
6.5%
r 1872
 
6.0%
l 1810
 
5.8%
d 913
 
2.9%
Other values (14) 5661
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 671
11.2%
A 653
10.9%
B 492
 
8.2%
R 489
 
8.2%
S 488
 
8.2%
M 484
 
8.1%
T 411
 
6.9%
P 403
 
6.7%
D 328
 
5.5%
L 248
 
4.1%
Other values (10) 1318
22.0%
Space Separator
ValueCountFrequency (%)
3606
100.0%
Other Punctuation
ValueCountFrequency (%)
. 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37384
91.0%
Common 3687
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3770
 
10.1%
s 3530
 
9.4%
e 3277
 
8.8%
i 3014
 
8.1%
o 2795
 
7.5%
n 2724
 
7.3%
t 2033
 
5.4%
r 1872
 
5.0%
l 1810
 
4.8%
d 913
 
2.4%
Other values (34) 11646
31.2%
Common
ValueCountFrequency (%)
3606
97.8%
. 81
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3770
 
9.2%
3606
 
8.8%
s 3530
 
8.6%
e 3277
 
8.0%
i 3014
 
7.3%
o 2795
 
6.8%
n 2724
 
6.6%
t 2033
 
4.9%
r 1872
 
4.6%
l 1810
 
4.4%
Other values (36) 12640
30.8%

home_team_errors
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58617886
Minimum0
Maximum5
Zeros1416
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:02:06.975429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80581712
Coefficient of variation (CV)1.3746949
Kurtosis2.0546943
Mean0.58617886
Median Absolute Deviation (MAD)0
Skewness1.4410193
Sum1442
Variance0.64934123
MonotonicityNot monotonic
2023-02-03T17:02:07.632650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1416
57.6%
1 732
29.8%
2 241
 
9.8%
3 57
 
2.3%
4 13
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 1416
57.6%
1 732
29.8%
2 241
 
9.8%
3 57
 
2.3%
4 13
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 13
 
0.5%
3 57
 
2.3%
2 241
 
9.8%
1 732
29.8%
0 1416
57.6%

home_team_hits
Real number (ℝ)

Distinct23
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6113821
Minimum0
Maximum22
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:02:08.382247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median8
Q311
95-th percentile15
Maximum22
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4386792
Coefficient of variation (CV)0.39931792
Kurtosis0.18147362
Mean8.6113821
Median Absolute Deviation (MAD)2
Skewness0.47623304
Sum21184
Variance11.824515
MonotonicityNot monotonic
2023-02-03T17:02:09.051225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 294
12.0%
7 275
11.2%
9 274
11.1%
6 257
10.4%
10 236
9.6%
11 194
7.9%
5 184
7.5%
12 165
6.7%
4 151
6.1%
13 96
 
3.9%
Other values (13) 334
13.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12
 
0.5%
2 33
 
1.3%
3 72
 
2.9%
4 151
6.1%
5 184
7.5%
6 257
10.4%
7 275
11.2%
8 294
12.0%
9 274
11.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 3
 
0.1%
20 1
 
< 0.1%
19 10
 
0.4%
18 17
 
0.7%
17 28
 
1.1%
16 31
 
1.3%
15 36
 
1.5%
14 89
3.6%
13 96
3.9%

home_team_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5203252
Minimum0
Maximum17
Zeros130
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:02:09.756474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1125024
Coefficient of variation (CV)0.68855719
Kurtosis0.83058099
Mean4.5203252
Median Absolute Deviation (MAD)2
Skewness0.92009549
Sum11120
Variance9.6876713
MonotonicityNot monotonic
2023-02-03T17:02:10.535619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 354
14.4%
2 312
12.7%
4 305
12.4%
5 301
12.2%
1 273
11.1%
6 207
8.4%
7 190
7.7%
0 130
 
5.3%
8 130
 
5.3%
9 79
 
3.2%
Other values (8) 179
7.3%
ValueCountFrequency (%)
0 130
 
5.3%
1 273
11.1%
2 312
12.7%
3 354
14.4%
4 305
12.4%
5 301
12.2%
6 207
8.4%
7 190
7.7%
8 130
 
5.3%
9 79
 
3.2%
ValueCountFrequency (%)
17 4
 
0.2%
16 5
 
0.2%
15 4
 
0.2%
14 16
 
0.7%
13 26
 
1.1%
12 33
 
1.3%
11 37
 
1.5%
10 54
2.2%
9 79
3.2%
8 130
5.3%

start_time
Real number (ℝ)

Distinct207
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.286165
Minimum11.083333
Maximum21.866667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T17:02:11.474625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum11.083333
5-th percentile12.766667
Q113.65
median19.116667
Q319.166667
95-th percentile19.283333
Maximum21.866667
Range10.783333
Interquartile range (IQR)5.5166667

Descriptive statistics

Standard deviation2.6709815
Coefficient of variation (CV)0.15451556
Kurtosis-1.1362629
Mean17.286165
Median Absolute Deviation (MAD)0.15
Skewness-0.81782639
Sum42523.967
Variance7.1341423
MonotonicityNot monotonic
2023-02-03T17:02:12.496248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.16666667 413
 
16.8%
19.18333333 213
 
8.7%
19.11666667 195
 
7.9%
19.13333333 138
 
5.6%
13.16666667 121
 
4.9%
13.18333333 82
 
3.3%
19.15 80
 
3.3%
19.25 66
 
2.7%
18.66666667 51
 
2.1%
19.1 50
 
2.0%
Other values (197) 1051
42.7%
ValueCountFrequency (%)
11.08333333 1
 
< 0.1%
11.11666667 1
 
< 0.1%
12.08333333 1
 
< 0.1%
12.1 5
 
0.2%
12.11666667 1
 
< 0.1%
12.13333333 2
 
0.1%
12.15 1
 
< 0.1%
12.16666667 21
0.9%
12.18333333 6
 
0.2%
12.2 1
 
< 0.1%
ValueCountFrequency (%)
21.86666667 1
< 0.1%
21.78333333 1
< 0.1%
21.51666667 1
< 0.1%
21.35 2
0.1%
21.25 1
< 0.1%
20.83333333 1
< 0.1%
20.8 1
< 0.1%
20.68333333 1
< 0.1%
20.66666667 1
< 0.1%
20.5 1
< 0.1%

venue
Categorical

Distinct31
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
progressivefield
 
89
wrigleyfield
 
89
dodgerstadium
 
86
rogerscentre
 
86
nationalspark
 
84
Other values (26)
2026 

Length

Max length29
Median length17
Mean length13.99878
Min length7

Characters and Unicode

Total characters34437
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowkauffmanstadium
2nd rowgreatamericanballpark
3rd roworioleparkatcamdenyards
4th rowturnerfield
5th rowchasefield

Common Values

ValueCountFrequency (%)
progressivefield 89
 
3.6%
wrigleyfield 89
 
3.6%
dodgerstadium 86
 
3.5%
rogerscentre 86
 
3.5%
nationalspark 84
 
3.4%
globelifeparkinarlington 83
 
3.4%
at&tpark 83
 
3.4%
fenwaypark 82
 
3.3%
kauffmanstadium 81
 
3.3%
greatamericanballpark 81
 
3.3%
Other values (21) 1616
65.7%

Length

2023-02-03T17:02:13.441842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
progressivefield 89
 
3.6%
wrigleyfield 89
 
3.6%
dodgerstadium 86
 
3.5%
rogerscentre 86
 
3.5%
nationalspark 84
 
3.4%
globelifeparkinarlington 83
 
3.4%
at&tpark 83
 
3.4%
fenwaypark 82
 
3.3%
safecofield 81
 
3.3%
orioleparkatcamdenyards 81
 
3.3%
Other values (21) 1616
65.7%

Most occurring characters

ValueCountFrequency (%)
a 4151
12.1%
i 3363
 
9.8%
e 3300
 
9.6%
r 2803
 
8.1%
l 2295
 
6.7%
d 1811
 
5.3%
n 1797
 
5.2%
t 1720
 
5.0%
s 1485
 
4.3%
o 1483
 
4.3%
Other values (16) 10229
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34113
99.1%
Other Punctuation 243
 
0.7%
Dash Punctuation 81
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4151
12.2%
i 3363
 
9.9%
e 3300
 
9.7%
r 2803
 
8.2%
l 2295
 
6.7%
d 1811
 
5.3%
n 1797
 
5.3%
t 1720
 
5.0%
s 1485
 
4.4%
o 1483
 
4.3%
Other values (13) 9905
29.0%
Other Punctuation
ValueCountFrequency (%)
. 160
65.8%
& 83
34.2%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34113
99.1%
Common 324
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4151
12.2%
i 3363
 
9.9%
e 3300
 
9.7%
r 2803
 
8.2%
l 2295
 
6.7%
d 1811
 
5.3%
n 1797
 
5.3%
t 1720
 
5.0%
s 1485
 
4.4%
o 1483
 
4.3%
Other values (13) 9905
29.0%
Common
ValueCountFrequency (%)
. 160
49.4%
& 83
25.6%
- 81
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4151
12.1%
i 3363
 
9.8%
e 3300
 
9.6%
r 2803
 
8.1%
l 2295
 
6.7%
d 1811
 
5.3%
n 1797
 
5.2%
t 1720
 
5.0%
s 1485
 
4.3%
o 1483
 
4.3%
Other values (16) 10229
29.7%

time_condition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Night Game
1664 
Day Game
796 

Length

Max length10
Median length10
Mean length9.3528455
Min length8

Characters and Unicode

Total characters23008
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight Game
2nd rowNight Game
3rd rowNight Game
4th rowNight Game
5th rowDay Game

Common Values

ValueCountFrequency (%)
Night Game 1664
67.6%
Day Game 796
32.4%

Length

2023-02-03T17:02:14.316959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T17:02:15.238597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
game 2460
50.0%
night 1664
33.8%
day 796
 
16.2%

Most occurring characters

ValueCountFrequency (%)
a 3256
14.2%
2460
10.7%
G 2460
10.7%
m 2460
10.7%
e 2460
10.7%
N 1664
7.2%
i 1664
7.2%
g 1664
7.2%
h 1664
7.2%
t 1664
7.2%
Other values (2) 1592
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15628
67.9%
Uppercase Letter 4920
 
21.4%
Space Separator 2460
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3256
20.8%
m 2460
15.7%
e 2460
15.7%
i 1664
10.6%
g 1664
10.6%
h 1664
10.6%
t 1664
10.6%
y 796
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
G 2460
50.0%
N 1664
33.8%
D 796
 
16.2%
Space Separator
ValueCountFrequency (%)
2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20548
89.3%
Common 2460
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3256
15.8%
G 2460
12.0%
m 2460
12.0%
e 2460
12.0%
N 1664
8.1%
i 1664
8.1%
g 1664
8.1%
h 1664
8.1%
t 1664
8.1%
D 796
 
3.9%
Common
ValueCountFrequency (%)
2460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3256
14.2%
2460
10.7%
G 2460
10.7%
m 2460
10.7%
e 2460
10.7%
N 1664
7.2%
i 1664
7.2%
g 1664
7.2%
h 1664
7.2%
t 1664
7.2%
Other values (2) 1592
6.9%

field_conditions
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
on grass
2293 
on turf
 
167

Length

Max length9
Median length9
Mean length8.9321138
Min length8

Characters and Unicode

Total characters21973
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row on grass
2nd row on grass
3rd row on grass
4th row on grass
5th row on grass

Common Values

ValueCountFrequency (%)
on grass 2293
93.2%
on turf 167
 
6.8%

Length

2023-02-03T17:02:15.859205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T17:02:16.664772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
on 2460
50.0%
grass 2293
46.6%
turf 167
 
3.4%

Most occurring characters

ValueCountFrequency (%)
4920
22.4%
s 4586
20.9%
o 2460
11.2%
n 2460
11.2%
r 2460
11.2%
g 2293
10.4%
a 2293
10.4%
t 167
 
0.8%
u 167
 
0.8%
f 167
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17053
77.6%
Space Separator 4920
 
22.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4586
26.9%
o 2460
14.4%
n 2460
14.4%
r 2460
14.4%
g 2293
13.4%
a 2293
13.4%
t 167
 
1.0%
u 167
 
1.0%
f 167
 
1.0%
Space Separator
ValueCountFrequency (%)
4920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17053
77.6%
Common 4920
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4586
26.9%
o 2460
14.4%
n 2460
14.4%
r 2460
14.4%
g 2293
13.4%
a 2293
13.4%
t 167
 
1.0%
u 167
 
1.0%
f 167
 
1.0%
Common
ValueCountFrequency (%)
4920
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4920
22.4%
s 4586
20.9%
o 2460
11.2%
n 2460
11.2%
r 2460
11.2%
g 2293
10.4%
a 2293
10.4%
t 167
 
0.8%
u 167
 
0.8%
f 167
 
0.8%

Interactions

2023-02-03T17:01:46.816113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:54.260754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:00.752397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:07.179804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:13.448239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:20.366142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:26.915363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:33.898808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:40.038891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:47.574607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:54.942721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:01.537426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:07.785594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:14.197501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:21.089170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:27.577307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:34.623114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:40.736040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:48.379392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:55.605454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:02.332795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:08.447709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:14.912094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:21.800038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:28.383879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:35.401222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:41.440946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:49.105642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:56.328488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:02.976844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:09.118144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:15.636982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:22.464232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:29.098347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:36.052745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:42.175537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:49.849311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:57.060594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:03.588502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:09.772464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:16.359701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:23.300271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:29.828205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:36.660469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:42.854478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:50.686464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:57.807619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:04.300233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:10.487373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:17.104492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:24.063601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:30.670176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:37.376937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:43.508648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:51.422427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:58.516954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:04.964379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:11.174887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:17.862980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:24.839054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:31.509422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:38.041112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:44.341184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:52.175234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:00:59.218628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:05.616659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:11.851500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:18.768209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:25.464653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:32.230595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:38.654263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:45.131743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:53.045808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:00.070306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:06.399282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:12.740875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:19.563726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:26.254933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:33.113712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:39.353196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-03T17:01:45.939291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-03T17:02:17.679483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
attendanceaway_team_errorsaway_team_hitsaway_team_runsgame_durationhome_team_errorshome_team_hitshome_team_runsstart_timeaway_teamhome_teamvenuetime_conditionfield_conditions
attendance1.0000.018-0.042-0.0490.050-0.0180.0010.024-0.0790.1190.4250.4260.1230.402
away_team_errors0.0181.0000.0330.0470.1310.0050.1450.2060.0160.0330.0000.0000.0000.000
away_team_hits-0.0420.0331.0000.7590.4870.1660.1010.046-0.0070.0000.0670.0670.0000.000
away_team_runs-0.0490.0470.7591.0000.4580.2570.0870.035-0.0030.0300.0640.0620.0000.000
game_duration0.0500.1310.4870.4581.0000.1520.3480.2460.0030.0320.0680.0660.0610.041
home_team_errors-0.0180.0050.1660.2570.1521.000-0.019-0.010-0.0270.0360.0490.0460.0000.000
home_team_hits0.0010.1450.1010.0870.348-0.0191.0000.747-0.0300.0400.0600.0590.0000.000
home_team_runs0.0240.2060.0460.0350.246-0.0100.7471.000-0.0050.0080.0380.0340.0000.000
start_time-0.0790.016-0.007-0.0030.003-0.027-0.030-0.0051.0000.0390.1990.2030.9950.092
away_team0.1190.0330.0000.0300.0320.0360.0400.0080.0391.0000.1790.1790.0000.251
home_team0.4250.0000.0670.0640.0680.0490.0600.0380.1990.1791.0001.0000.0980.994
venue0.4260.0000.0670.0620.0660.0460.0590.0340.2030.1791.0001.0000.0970.994
time_condition0.1230.0000.0000.0000.0610.0000.0000.0000.9950.0000.0980.0971.0000.021
field_conditions0.4020.0000.0000.0000.0410.0000.0000.0000.0920.2510.9940.9940.0211.000

Missing values

2023-02-03T17:01:54.489915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-03T17:01:56.185039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

attendanceaway_teamaway_team_errorsaway_team_hitsaway_team_runsgame_durationhome_teamhome_team_errorshome_team_hitshome_team_runsstart_timevenuetime_conditionfield_conditions
040030.0New York Mets1733.216667Kansas City Royals09419.633333kauffmanstadiumNight Gameon grass
121621.0Philadelphia Phillies0522.383333Cincinnati Reds08319.183333greatamericanballparkNight Gameon grass
212622.0Minnesota Twins0523.183333Baltimore Orioles09419.116667orioleparkatcamdenyardsNight Gameon grass
318531.0Washington Nationals0832.883333Atlanta Braves18119.166667turnerfieldNight Gameon grass
418572.0Colorado Rockies1842.650000Arizona Diamondbacks08312.666667chasefieldDay Gameon grass
528386.0Seattle Mariners111103.500000Texas Rangers17219.116667globelifeparkinarlingtonNight Gameon grass
612757.0Toronto Blue Jays0923.116667Tampa Bay Rays17319.166667tropicanafieldNight Gameon turf
728329.0Los Angeles Dodgers0632.600000San Diego Padres12019.183333petcoparkNight Gameon grass
826049.0St. Louis Cardinals1853.450000Pittsburgh Pirates212619.133333pncparkNight Gameon grass
910478.0Chicago White Sox01153.466667Oakland Athletics010419.133333oakland-alamedacountycoliseumNight Gameon grass
attendanceaway_teamaway_team_errorsaway_team_hitsaway_team_runsgame_durationhome_teamhome_team_errorshome_team_hitshome_team_runsstart_timevenuetime_conditionfield_conditions
245343683.0Philadelphia Phillies2622.933333Cincinnati Reds06616.183333greatamericanballparkDay Gameon grass
245445785.0Minnesota Twins0722.800000Baltimore Orioles010316.766667orioleparkatcamdenyardsDay Gameon grass
245548282.0Washington Nationals0843.383333Atlanta Braves24316.216667turnerfieldDay Gameon grass
245648165.0Colorado Rockies015104.183333Arizona Diamondbacks012518.700000chasefieldNight Gameon grass
245744020.0Chicago Cubs01193.133333Los Angeles Angels of Anaheim13019.133333angelstadiumofanaheimNight Gameon grass
245831042.0Toronto Blue Jays2752.850000Tampa Bay Rays17316.150000tropicanafieldDay Gameon turf
245939500.0St. Louis Cardinals0513.033333Pittsburgh Pirates19413.250000pncparkDay Gameon grass
246020098.0San Francisco Giants0633.316667Milwaukee Brewers29412.683333millerparkDay Gameon grass
246117883.0Detroit Tigers01373.366667Miami Marlins110316.950000marlinsparkDay Gameon grass
246210298.0Boston Red Sox11063.483333Cleveland Indians09718.366667progressivefieldNight Gameon grass